research-article

Public Access

Gesture Recognition Using Ambient Light

Authors:

Raghav H. Venkatnarayan,

Muhammad ShahzadAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 1

Article No.: 40, Pages 1 - 28

https://doi.org/10.1145/3191772

Published: 26 March 2018 Publication History

Abstract

There is a growing interest in the scientific community to develop techniques for humans to communicate with the computing that is embedding into our environments. Researchers are already exploring ubiquitous modalities, such as radio frequency signals, to develop gesture recognition systems. In this paper, we explore another such modality, namely ambient light, and develop LiGest, an ambient light based gesture recognition system. The key property of LiGest is that it is agnostic to lighting conditions, position and orientation of user, and who performs the gestures. The general idea behind LiGest is that when a user performs different gestures, the shadows of the user move in unique patterns. LiGest first learns these patterns using training samples and then recognizes unknown samples by matching them with the learnt patterns. To capture these patterns, LiGest uses a grid of light sensors deployed on floor. While the general idea behind LiGest seems straightforward, it is actually very challenging to put it into practice because the intensity, size, and number of shadows of a user are not fixed and depend highly on the position and orientation of a user as well as on the intensity, position, and number of light sources. We developed a prototype of LiGest using commercially available light sensors and extensively evaluated it with the help of 20 volunteers. Our results show that LiGest achieves an average accuracy of 96.36% across all volunteers.

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 1

March 2018

1370 pages

EISSN:2474-9567

DOI:10.1145/3200905

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Association for Computing Machinery

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Publication History

Published: 26 March 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 August 2017

Published in IMWUT Volume 2, Issue 1

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Chen KWang LHuang YWu KWang L(2025)Optical Sensing-Based Intelligent Toothbrushing Monitoring SystemIEEE Transactions on Mobile Computing10.1109/TMC.2024.347945524:3(1417-1436)Online publication date: 1-Mar-2025
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